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base_model: Qwen/Qwen3.5-0.8B
library_name: peft
license: other
tags:
- lora
- peft
- fine-tuned
- adaption
---
# adaption_general_knowledge_qa
## Model Training
A LORA fine-tune of `Qwen/Qwen3.5-0.8B`. This model was trained with supervised fine-tuning (SFT) using [Adaption](https://adaptionlabs.ai)'s AutoScientist on the general_knowledge_qa dataset.
### AutoScientist Config
```json
{
"finetune_job_id": "69d2d2ca-5984-45d7-8e07-f24a59d39424",
"training_experiment_id": "b3805568-3565-4d1c-ad72-4e8173aee82f",
"original_model_name": "Qwen/Qwen3.5-0.8B",
"trained_model_name": "adaption_general_knowledge_qa",
"training_method": "sft",
"training_type": "lora",
"data_format": "chat",
"hyperparams": {
"lora": "true",
"lora_r": 16,
"n_evals": 5,
"n_epochs": 1,
"batch_size": "max",
"lora_alpha": 32,
"lora_dropout": 0,
"min_lr_ratio": 0.1,
"warmup_ratio": 0.03,
"weight_decay": 0,
"learning_rate": 0.00001,
"max_grad_norm": 2,
"base_model_size": "0.8B",
"train_on_inputs": "false",
"training_method": "sft",
"lr_scheduler_type": "cosine",
"scheduler_num_cycles": 0.5,
"lora_trainable_modules": "q_proj,k_proj,v_proj,o_proj"
}
}
```
## Training Data
The model was fine-tuned on 3,588 rows of adapted data with the following domain distribution: history (11%), sports (10%), science (9%), geography (7%), entertainment (6%), music (6%), animal-nature (5%), cooking (5%), culture (4%), travel (3%), transportation (3%), technology (2%), corporate-business (2%), governance (2%), medical (2%), games (2%), writing-editing-communication (2%), fitness-sports (2%), academic-education (1%), math (1%), code (1%), language (1%), agriculture (1%), how-to (1%), personal-finance (1%), art (1%), career-workplace (1%), religion (1%), product-advice (1%), parenting-family (1%), personal-growth (1%), fashion-beauty (1%), marketing (0%), architecture-design (0%), data-analysis-visualization (0%), other (0%), legal (0%), dating (0%), market-analysis (0%), news (0%), hr (0%), social (0%), roleplay (0%), literature (0%).
## Model Evaluation
The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.
## How to use
```bash
pip install torch transformers peft
```
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
BASE = "Qwen/Qwen3.5-0.8B"
ADAPTER = "<this-repo-id>"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16
base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()
tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.inference_mode():
out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
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